A Flexible Software-Hardware Framework for Brain EEG Multiple Artifact Identification

Simple item page
dc.contributor.authorKhatwani, Mohit
dc.contributor.authorRashid, Hasib-Al
dc.contributor.authorPaneliya, Hirenkumar
dc.contributor.authorHorton, Mark
dc.contributor.authorHomayoun, Houman
dc.contributor.authorWaytowich, Nicholas
dc.contributor.authorHairston, W. David
dc.contributor.authorMohsenin, Tinoosh
dc.date.accessioned2021-02-15T18:44:21Z
dc.date.available2021-02-15T18:44:21Z
dc.date.issued2020
dc.description.abstractThis chapter presents an energy efficient and flexible multichannel Electroencephalogram (EEG) artifact identification network and its hardware using depthwise and separable convolutional neural networks (DS-CNN). EEG signals are recordings of the brain activities. The EEG recordings that are not originated from cerebral activities are termed as artifacts. Our proposed model does not need expert knowledge for feature extraction or pre-processing of EEG data and has a very efficient architecture implementable on mobile devices. The proposed network can be reconfigured for any number of EEG channel and artifact classes. Experiments were done with the proposed model with the goal of maximizing the identification accuracy while minimizing the weight parameters and required number of operations.en_US
dc.description.urihttp://eehpc.csee.umbc.edu/publications/pdf/2020/A_Flexible_Software_Hardware_Framework_for_Brain_EEG_Multiple_Artifact_Identification.pdfen_US
dc.format.extent26 pagesen_US
dc.genrebook chapters preprintsen_US
dc.identifierdoi:10.13016/m27g58-pnnj
dc.identifier.citationKhatwani, Mohit; Rashid, Hasib-Al; Paneliya, Hirenkumar; Horton, Mark; Homayoun, Houman; Waytowich, Nicholas; Hairston, W. David; Mohsenin, Tinoosh; A Flexible Software-Hardware Framework for Brain EEG Multiple Artifact Identification; UMBC Energy Efficient High Performance Computing Lab (2020); http://eehpc.csee.umbc.edu/publications/pdf/2020/A_Flexible_Software_Hardware_Framework_for_Brain_EEG_Multiple_Artifact_Identification.pdfen_US
dc.identifier.urihttp://hdl.handle.net/11603/21021
dc.language.isoen_USen_US
dc.publisherUMBC Energy Efficient High Performance Computing Laben_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsPublic Domain Mark 1.0*
dc.rightsThis work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/*
dc.subjectUMBC Energy Efficient High Performance Computing Laben_US
dc.titleA Flexible Software-Hardware Framework for Brain EEG Multiple Artifact Identificationen_US
dc.typeTexten_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
A_Flexible_Software_Hardware_Framework_for_Brain_EEG_Multiple_Artifact_Identification.pdf
Size:
5.04 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.56 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection
UMBC Student Collection